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Transfer Learning using Computational Intelligence
Transfer Learning using Computational Intelligence

... quickly and effectively. The fundamental motivation for transfer learning in the field of machine learning focuses on the need for lifelong machine learning methods that retain and reuse previously learned knowledge. Research on transfer learning has been undertaken since 1995 under a variety of nam ...
Lecture Notes for Physics 229: Quantum Information and Computation
Lecture Notes for Physics 229: Quantum Information and Computation

Fast Converging Path Integrals for Time
Fast Converging Path Integrals for Time

... calculate precisely the condensation temperature, the ground-state occupancy, and time-of-flight absorption pictures even in the delicate regime of a critical and an overcritical rotation. If one needs a large numbers of accurate energy eigenvalues and eigenstates, as in this case, higher-order effe ...
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A classical treatment of optical tunneling in plasmonic gaps
A classical treatment of optical tunneling in plasmonic gaps

... through D(r, u) ¼ E(r, u) + 4pP(r, u) ¼ 3(r, u)E(r, u), where the medium polarisation P veries 4pP(r, u) ¼ (3(r, u)  1)E(r, u) (in atomic units). Equivalently, P(r, u) ¼ c(r, u)E(r, u), where c(r, u) ¼ (3(r, u)  1)/4p is the medium polarisability. For an isotropic and homogeneous medium, 3 and c ...
Bulk Entanglement Spectrum Reveals Quantum
Bulk Entanglement Spectrum Reveals Quantum

... Topological phases of matter are characterized by quantized physical properties that arise from topological quantum numbers. For instance, the quantized Hall conductance of an integer quantum Hall state is determined by its Chern number [1], the quantized magnetoelectric response of a topological in ...
Multiagent Learning: Basics, Challenges, and
Multiagent Learning: Basics, Challenges, and

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Focus on out-of-equilibrium dynamics in strongly interacting one

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Aalborg Universitet Quantum Organizational World-Making through Material Emobided Storytelling Practices

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Endomorphism Bialgebras of Diagrams and of Non

... construct explicit examples of such and check all the necessary properties. This gets even more complicated if we have to verify that something like a comodule algebra over a bialgebra is given. Bialgebras and comodule algebras, however, arise in a very natural way in non-commutative geometry and in ...
Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition
Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition

... used for reasoning, decision making, predicting things, communicating etc. Reinforcement learning: The machine can also produce actions a1, a2, . . . which affect the state of the world, and receives rewards (or punishments) r1, r2, . . .. Its goal is to learn to act in a way that maximises rewards ...
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Entanglement in single-particle systems

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Information measures, entanglement and quantum evolution Claudia Zander

... aspects of thermodynamics and other areas of physics [22]. Information is something that is encoded in a physical state of a system and a computation is something that can be carried out on a physically realizable device. In order to quantify information one will need a measure of how much informati ...
Population Monte Carlo algorithms
Population Monte Carlo algorithms

... be replaced by sequential runs each of which corresponds to the simulation of a walker. In some references, a part of STEP 3 is separately discussed as a “population control” procedure (e.g., [Kalos 62, Grassberger 98]) and/or a part of STEP 3 is merged into STEP 1 (e.g., [Kalos 62]). In such cases, ...
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Smooth Scaling of Valence Electronic Properties in Fullerenes: From

Quantum Computing - Lecture Notes - Washington
Quantum Computing - Lecture Notes - Washington

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Quantum Computing

... This is the remarkable thing about entanglement. By measuring one qubit we can affect the probability amplitudes of the other qubits in a system! How to think about this process in an abstract way is an open challenge in quantum computing. The difficulty is the lack of any classical analog. One usef ...
Von Neumann`s Impossibility Proof: Mathematics in - Philsci
Von Neumann`s Impossibility Proof: Mathematics in - Philsci

... Though both theories ultimately yield the same physical predictions, they start from very different principles and are also mathematically disparate. In particular, in matrix mechanics one has to operate with discrete quantities (components of matrices) whereas wave mechanics has the form of a conti ...
Optimum topology of quasi-one dimensional nonlinear optical
Optimum topology of quasi-one dimensional nonlinear optical

Preparing projected entangled pair states on a quantum computer
Preparing projected entangled pair states on a quantum computer

... Projection onto the next ground state is performed using Phase Estimation[5] We perform a binary measurement on the energy register (zero or non-zero) If the outcome is zero, we have perpared the desired ground state Else, we undo the measurement using the well-known Marriot-Watrous trick[6] and re- ...
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A Topos for Algebraic Quantum Theory
A Topos for Algebraic Quantum Theory

... This introduction is intended for both mathematical physicists and topos theorists. We apologize in advance for stating the obvious for one or the other of these groups at various points, but we hope that most of it is interesting to both communities. ...
using standard syste - the Max Planck Institute for the Physics of
using standard syste - the Max Planck Institute for the Physics of

... lying chaotic classical dynamics, in particular if the system is highly excited in an energy regime where infinitely many resonances exist. In this respect, twoelectron atoms are of basic interest not only in atomic physics but also for the development of concepts of quantum chaos (see Berry, 1983; ...
Categorical Models for Quantum Computing
Categorical Models for Quantum Computing

... of the measurement and the resulting system is in one of the base states. We do not know in advance which of the base states. Even if we do know in which state the quantum system is we can only calculate the probability that it will be in one of the base states after the measurement. As a result, we ...
Spin and Charge Transport through Driven Quantum Dot Systems
Spin and Charge Transport through Driven Quantum Dot Systems

... effect in two dimensional electron gases in semiconductor structures were reported[1]. There, for the first time, quantized magnitudes were reached in mesoscopic artificial devices of hundreds of µm. From that moment, the size of fabricated solid state structures has rapidly decreased by the impulse ...
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Quantum machine learning

Quantum machine learning is a newly emerging interdisciplinary research area between quantum physics and computer science that summarises efforts to combine quantum mechanics with methods of machine learning. Quantum machine learning models or algorithms intend to use the advantages of quantum information in order to improve classical methods of machine learning, for example by developing efficient implementations of expensive classical algorithms on a quantum computer. However, quantum machine learning also includes the vice versa approach, namely applying classical methods of machine learning to quantum information theory.Although yet in its infancy, quantum machine learning is met with high expectations of providing a solution for big data analysis using the ‘parallel’ power of quantum computation. This trend is underlined by recent investments of companies such as Google and Microsoft into quantum computing hardware and research. However, quantum machine learning is still in its infancy and requires more theoretical foundations as well as solid scientific results in order to mature to a full academic discipline.
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